In summary, the differences of the proposed method with the existed works [1]}, [2]}, [3]}, [4]} are summarized as follows.
(1) Unlike the previous methods which train the neural network to learn the labels of each pixel, the output of our method is the latent uniform version of the input images, which represents an internally enhanced version of the image.
(2) Our method can be used iteratively to refine the outputs since the output of the method is the improved version of the input. However, the previous methods are based on intensity probabilities per pixel, which are hard to optimize iteratively.
(3) In our approach we make a distinction between handling degradations and the handling of the binarization.
The neural network is trained to correct degradations, while the final binarization is achieved by the efficient global-threshold Otsu method.
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